Abstract

Introduction

Clinicians use different breast cancer risk models for patients considered at average
and above-average risk, based largely on their family histories and genetic factors.
We used longitudinal cohort data from women whose breast cancer risks span the full
spectrum to determine the genetic and nongenetic covariates that differentiate the
performance of two commonly used models that include nongenetic factors - BCRAT, also
called Gail model, generally used for patients with average risk and IBIS, also called
Tyrer Cuzick model, generally used for patients with above-average risk.

Methods

We evaluated the performance of the BCRAT and IBIS models as currently applied in
clinical settings for 10-year absolute risk of breast cancer, using prospective data
from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer.
This cohort spans the continuum of breast cancer risk, with some subjects at lower
than average population risk. Therefore, the wide variation in individual risk makes
it an interesting population to examine model performance across subgroups of women.
For model calibration, we divided the cohort into quartiles of model-assigned risk
and compared differences between assigned and observed risks using the Hosmer-Lemeshow
(HL) chi-squared statistic. For model discrimination, we computed the area under the
receiver operator curve (AUC) and the case risk percentiles (CRPs).

Results

The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to
79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were
lower than the cohort's 10-year cumulative probability of developing breast cancer
(6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and
observed risks was better for IBIS (HL X42 = 7.2, P value 0.13) than BCRAT (HL X42 = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI =
63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost
all covariate-specific subgroups, BCRAT mean risks were significantly lower than the
observed risks, while IBIS risks showed generally good agreement with observed risks,
even in the subgroups of women considered at average risk (for example, no family
history of breast cancer, BRCA1/2 mutation negative).

Conclusions

Models developed using extended family history and genetic data, such as the IBIS
model, also perform well in women considered at average risk (for example, no family
history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information
may improve performance in women across the breast cancer risk continuum.